Visible deep learning reveals how genes shape cell characteristics

The inner workings of a cell can be revealed by a new deep-learning computer algorithm that has accessible processes, reports a paper published online this week in Nature Methods.

Artificial intelligence can perform many complex tasks typically done by humans, such as recognizing faces, translating languages, and playing games. Deep-learning networks, also known as artificial neural networks, are increasingly being used to automate biological data analysis.

A challenge with deep-learning models is that they are typically ‘black boxes’, meaning that the process by which a model performs a task is not easily identifiable. For biological applications, the ability to examine the way deep-learning models recognize and process the data they analyze could help researchers better understand the biology behind these data.

Trey Ideker and colleagues created a ‘visible’ artificial neural network by mapping the structure of a deep-learning algorithm onto the known structures of molecular systems within cells. The authors show that once the model is trained, it can predict the physical effects of genetic changes. Moreover, as the model’s components are accessible, it can also provide insight about the mechanisms underlying the relationship between genes and physical characteristics. The researchers also show how such a visible neural network can be used to understand genetic logic, identify which molecular systems are important for certain physical characteristics, and discover new processes within the cell.